Quantitative assessment of gigapixel whole slide images remains challenging due to morphological diversity and human intervention during slide acquisition and processing. While AI-assisted diagnostics hold promise for improving healthcare accessibility and quality, practical application is hindered by difficulties in faithfully representing histomorphological heterogeneity and scaling AI models for deployment. Prevailing approaches predominantly rely on weakly supervised algorithms based on homogeneous graph, which typically decouple diagnostic models from the underlying hardware, resulting in lack of an efficient, closed-loop, end-to-end system. Here, we propose a portable integrated system: the Intelligent Pathology Whole-Slide Analyzer (iPathWS analyzer), which embeds a Spectral Heterogeneity Engine Network and interfaces directly with a slide scanner to form a unified platform for WSI inference. The system seamlessly overlays explainable heatmaps onto slides, enabling intuitive AI integration into routine clinical workflows in a closed-loop manner. We validate the effectiveness of the iPathWS analyzer across four distinct classification tasks spanned by three independent pathology datasets, including multi-center crossover studies. Furthermore, the model produces highly detailed and interpretable heatmaps capable of detecting tiny lesions with the size of 188.5× 280.2μm², which are often imperceptible by human observers. Overall, the iPathWS Analyzer demonstrates consistent and substantial improvements in diagnostic performance, offering a scalable and robust platform for next-generation computer-aided pathology.
Liang et al. (Thu,) studied this question.